<template>
	<div class="page">
		<div id="myChart" :style="{ width: '900px', height: '600px' }"></div>

		<section style="padding: 40px 0;"></section>
		<div id="myChart2" :style="{ width: '900px', height: '600px' }"></div>

		<section style="padding: 40px 0;"></section>
		<div id="myChart3" :style="{ width: '900px', height: '600px' }"></div>

		<section style="padding: 40px 0;"></section>
		<div id="myChart4" :style="{ width: '900px', height: '600px' }"></div>
	</div>
</template>

<script>
import echarts from 'echarts'; //百度echarts

export default {
	data() {
		return {
			value: new Date()
		};
	},
	created() {},
	mounted() {
		this.init();
		this.init2();
		this.init3();
		this.init4();
	},
	methods: {
		init() {
			document.getElementById('myChart').setAttribute('_echarts_instance_', '');
			var myChart = echarts.init(document.getElementById('myChart'));
			var colors = ['#5793f3', '#d14a61', '#675bba'];
			myChart.setOption({
				color: colors,
				tooltip: {
					trigger: 'axis',
					axisPointer: {
						type: 'cross'
					}
				},
				grid: {
					right: '20%'
				},
				toolbox: {
					feature: {
						dataView: {
							show: true,
							readOnly: false
						},
						restore: {
							show: true
						},
						saveAsImage: {
							show: true
						}
					}
				},
				legend: {
					data: ['蒸发量', '降水量', '平均温度']
				},
				xAxis: [
					{
						type: 'category',
						axisTick: {
							alignWithLabel: true
						},
						data: ['1月', '2月', '3月', '4月', '5月', '6月', '7月', '8月', '9月', '10月', '11月', '12月']
					}
				],
				yAxis: [
					{
						type: 'value',
						name: '蒸发量',
						min: 0,
						max: 250,
						position: 'right',
						axisLine: {
							lineStyle: {
								color: colors[0]
							}
						},
						axisLabel: {
							formatter: '{value} ml'
						}
					},
					{
						type: 'value',
						name: '降水量',
						min: 0,
						max: 250,
						position: 'right',
						offset: 80,
						axisLine: {
							lineStyle: {
								color: colors[1]
							}
						},
						axisLabel: {
							formatter: '{value} ml'
						}
					},
					{
						type: 'value',
						name: '温度',
						min: 0,
						max: 25,
						position: 'left',
						axisLine: {
							lineStyle: {
								color: colors[2]
							}
						},
						axisLabel: {
							formatter: '{value} °C'
						}
					}
				],
				series: [
					{
						name: '蒸发量',
						type: 'bar',
						data: [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
					},
					{
						name: '降水量',
						type: 'bar',
						yAxisIndex: 1,
						data: [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
					},
					{
						name: '平均温度',
						type: 'line',
						yAxisIndex: 2,
						data: [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
					}
				]
			});
		},
		init2() {
			document.getElementById('myChart2').setAttribute('_echarts_instance_', '');
			var myChart = echarts.init(document.getElementById('myChart2'));
			myChart.setOption({
				tooltip: {
					trigger: 'axis',
					axisPointer: {
						// 坐标轴指示器，坐标轴触发有效
						type: 'shadow' // 默认为直线，可选为：'line' | 'shadow'
					}
				},
				legend: {
					data: ['直接访问', '邮件营销', '联盟广告', '视频广告', '搜索引擎', '百度', '谷歌', '必应', '其他']
				},
				grid: {
					left: '3%',
					right: '4%',
					bottom: '3%',
					containLabel: true
				},
				xAxis: [
					{
						type: 'category',
						data: ['周一', '周二', '周三', '周四', '周五', '周六', '周日']
					}
				],
				yAxis: [
					{
						type: 'value'
					}
				],
				series: [
					{
						name: '直接访问',
						type: 'bar',
						data: [320, 332, 301, 334, 390, 330, 320]
					},
					{
						name: '邮件营销',
						type: 'bar',
						stack: '广告',
						data: [120, 132, 101, 134, 90, 230, 210]
					},
					{
						name: '联盟广告',
						type: 'bar',
						stack: '广告',
						data: [220, 182, 191, 234, 290, 330, 310]
					},
					{
						name: '视频广告',
						type: 'bar',
						stack: '广告',
						data: [150, 232, 201, 154, 190, 330, 410]
					},
					{
						name: '搜索引擎',
						type: 'bar',
						data: [862, 1018, 964, 1026, 1679, 1600, 1570],
						markLine: {
							lineStyle: {
								type: 'dashed'
							},
							data: [
								[
									{
										type: 'min'
									},
									{
										type: 'max'
									}
								]
							]
						}
					},
					{
						name: '百度',
						type: 'bar',
						barWidth: 5,
						stack: '搜索引擎',
						data: [620, 732, 701, 734, 1090, 1130, 1120]
					},
					{
						name: '谷歌',
						type: 'bar',
						stack: '搜索引擎',
						data: [120, 132, 101, 134, 290, 230, 220]
					},
					{
						name: '必应',
						type: 'bar',
						stack: '搜索引擎',
						data: [60, 72, 71, 74, 190, 130, 110]
					},
					{
						name: '其他',
						type: 'bar',
						stack: '搜索引擎',
						data: [62, 82, 91, 84, 109, 110, 120]
					}
				]
			});
		},
		init3() {
			document.getElementById('myChart3').setAttribute('_echarts_instance_', '');
			var myChart = echarts.init(document.getElementById('myChart3'));
			var dataMap = {};
			function dataFormatter(obj) {
				var pList = [
					'北京',
					'天津',
					'河北',
					'山西',
					'内蒙古',
					'辽宁',
					'吉林',
					'黑龙江',
					'上海',
					'江苏',
					'浙江',
					'安徽',
					'福建',
					'江西',
					'山东',
					'河南',
					'湖北',
					'湖南',
					'广东',
					'广西',
					'海南',
					'重庆',
					'四川',
					'贵州',
					'云南',
					'西藏',
					'陕西',
					'甘肃',
					'青海',
					'宁夏',
					'新疆'
				];
				var temp;
				for (var year = 2002; year <= 2011; year++) {
					var max = 0;
					var sum = 0;
					temp = obj[year];
					for (var i = 0, l = temp.length; i < l; i++) {
						max = Math.max(max, temp[i]);
						sum += temp[i];
						obj[year][i] = {
							name: pList[i],
							value: temp[i]
						};
					}
					obj[year + 'max'] = Math.floor(max / 100) * 100;
					obj[year + 'sum'] = sum;
				}
				return obj;
			}

			dataMap.dataGDP = dataFormatter({
				//max : 60000,
				2011: [
					16251.93,
					11307.28,
					24515.76,
					11237.55,
					14359.88,
					22226.7,
					10568.83,
					12582,
					19195.69,
					49110.27,
					32318.85,
					15300.65,
					17560.18,
					11702.82,
					45361.85,
					26931.03,
					19632.26,
					19669.56,
					53210.28,
					11720.87,
					2522.66,
					10011.37,
					21026.68,
					5701.84,
					8893.12,
					605.83,
					12512.3,
					5020.37,
					1670.44,
					2102.21,
					6610.05
				],
				2010: [
					14113.58,
					9224.46,
					20394.26,
					9200.86,
					11672,
					18457.27,
					8667.58,
					10368.6,
					17165.98,
					41425.48,
					27722.31,
					12359.33,
					14737.12,
					9451.26,
					39169.92,
					23092.36,
					15967.61,
					16037.96,
					46013.06,
					9569.85,
					2064.5,
					7925.58,
					17185.48,
					4602.16,
					7224.18,
					507.46,
					10123.48,
					4120.75,
					1350.43,
					1689.65,
					5437.47
				],
				2009: [
					12153.03,
					7521.85,
					17235.48,
					7358.31,
					9740.25,
					15212.49,
					7278.75,
					8587,
					15046.45,
					34457.3,
					22990.35,
					10062.82,
					12236.53,
					7655.18,
					33896.65,
					19480.46,
					12961.1,
					13059.69,
					39482.56,
					7759.16,
					1654.21,
					6530.01,
					14151.28,
					3912.68,
					6169.75,
					441.36,
					8169.8,
					3387.56,
					1081.27,
					1353.31,
					4277.05
				],
				2008: [
					11115,
					6719.01,
					16011.97,
					7315.4,
					8496.2,
					13668.58,
					6426.1,
					8314.37,
					14069.87,
					30981.98,
					21462.69,
					8851.66,
					10823.01,
					6971.05,
					30933.28,
					18018.53,
					11328.92,
					11555,
					36796.71,
					7021,
					1503.06,
					5793.66,
					12601.23,
					3561.56,
					5692.12,
					394.85,
					7314.58,
					3166.82,
					1018.62,
					1203.92,
					4183.21
				],
				2007: [
					9846.81,
					5252.76,
					13607.32,
					6024.45,
					6423.18,
					11164.3,
					5284.69,
					7104,
					12494.01,
					26018.48,
					18753.73,
					7360.92,
					9248.53,
					5800.25,
					25776.91,
					15012.46,
					9333.4,
					9439.6,
					31777.01,
					5823.41,
					1254.17,
					4676.13,
					10562.39,
					2884.11,
					4772.52,
					341.43,
					5757.29,
					2703.98,
					797.35,
					919.11,
					3523.16
				],
				2006: [
					8117.78,
					4462.74,
					11467.6,
					4878.61,
					4944.25,
					9304.52,
					4275.12,
					6211.8,
					10572.24,
					21742.05,
					15718.47,
					6112.5,
					7583.85,
					4820.53,
					21900.19,
					12362.79,
					7617.47,
					7688.67,
					26587.76,
					4746.16,
					1065.67,
					3907.23,
					8690.24,
					2338.98,
					3988.14,
					290.76,
					4743.61,
					2277.35,
					648.5,
					725.9,
					3045.26
				],
				2005: [
					6969.52,
					3905.64,
					10012.11,
					4230.53,
					3905.03,
					8047.26,
					3620.27,
					5513.7,
					9247.66,
					18598.69,
					13417.68,
					5350.17,
					6554.69,
					4056.76,
					18366.87,
					10587.42,
					6590.19,
					6596.1,
					22557.37,
					3984.1,
					918.75,
					3467.72,
					7385.1,
					2005.42,
					3462.73,
					248.8,
					3933.72,
					1933.98,
					543.32,
					612.61,
					2604.19
				],
				2004: [
					6033.21,
					3110.97,
					8477.63,
					3571.37,
					3041.07,
					6672,
					3122.01,
					4750.6,
					8072.83,
					15003.6,
					11648.7,
					4759.3,
					5763.35,
					3456.7,
					15021.84,
					8553.79,
					5633.24,
					5641.94,
					18864.62,
					3433.5,
					819.66,
					3034.58,
					6379.63,
					1677.8,
					3081.91,
					220.34,
					3175.58,
					1688.49,
					466.1,
					537.11,
					2209.09
				],
				2003: [
					5007.21,
					2578.03,
					6921.29,
					2855.23,
					2388.38,
					6002.54,
					2662.08,
					4057.4,
					6694.23,
					12442.87,
					9705.02,
					3923.11,
					4983.67,
					2807.41,
					12078.15,
					6867.7,
					4757.45,
					4659.99,
					15844.64,
					2821.11,
					713.96,
					2555.72,
					5333.09,
					1426.34,
					2556.02,
					185.09,
					2587.72,
					1399.83,
					390.2,
					445.36,
					1886.35
				],
				2002: [
					4315,
					2150.76,
					6018.28,
					2324.8,
					1940.94,
					5458.22,
					2348.54,
					3637.2,
					5741.03,
					10606.85,
					8003.67,
					3519.72,
					4467.55,
					2450.48,
					10275.5,
					6035.48,
					4212.82,
					4151.54,
					13502.42,
					2523.73,
					642.73,
					2232.86,
					4725.01,
					1243.43,
					2312.82,
					162.04,
					2253.39,
					1232.03,
					340.65,
					377.16,
					1612.6
				]
			});

			dataMap.dataPI = dataFormatter({
				//max : 4000,
				2011: [
					136.27,
					159.72,
					2905.73,
					641.42,
					1306.3,
					1915.57,
					1277.44,
					1701.5,
					124.94,
					3064.78,
					1583.04,
					2015.31,
					1612.24,
					1391.07,
					3973.85,
					3512.24,
					2569.3,
					2768.03,
					2665.2,
					2047.23,
					659.23,
					844.52,
					2983.51,
					726.22,
					1411.01,
					74.47,
					1220.9,
					678.75,
					155.08,
					184.14,
					1139.03
				],
				2010: [
					124.36,
					145.58,
					2562.81,
					554.48,
					1095.28,
					1631.08,
					1050.15,
					1302.9,
					114.15,
					2540.1,
					1360.56,
					1729.02,
					1363.67,
					1206.98,
					3588.28,
					3258.09,
					2147,
					2325.5,
					2286.98,
					1675.06,
					539.83,
					685.38,
					2482.89,
					625.03,
					1108.38,
					68.72,
					988.45,
					599.28,
					134.92,
					159.29,
					1078.63
				],
				2009: [
					118.29,
					128.85,
					2207.34,
					477.59,
					929.6,
					1414.9,
					980.57,
					1154.33,
					113.82,
					2261.86,
					1163.08,
					1495.45,
					1182.74,
					1098.66,
					3226.64,
					2769.05,
					1795.9,
					1969.69,
					2010.27,
					1458.49,
					462.19,
					606.8,
					2240.61,
					550.27,
					1067.6,
					63.88,
					789.64,
					497.05,
					107.4,
					127.25,
					759.74
				],
				2008: [
					112.83,
					122.58,
					2034.59,
					313.58,
					907.95,
					1302.02,
					916.72,
					1088.94,
					111.8,
					2100.11,
					1095.96,
					1418.09,
					1158.17,
					1060.38,
					3002.65,
					2658.78,
					1780,
					1892.4,
					1973.05,
					1453.75,
					436.04,
					575.4,
					2216.15,
					539.19,
					1020.56,
					60.62,
					753.72,
					462.27,
					105.57,
					118.94,
					691.07
				],
				2007: [
					101.26,
					110.19,
					1804.72,
					311.97,
					762.1,
					1133.42,
					783.8,
					915.38,
					101.84,
					1816.31,
					986.02,
					1200.18,
					1002.11,
					905.77,
					2509.14,
					2217.66,
					1378,
					1626.48,
					1695.57,
					1241.35,
					361.07,
					482.39,
					2032,
					446.38,
					837.35,
					54.89,
					592.63,
					387.55,
					83.41,
					97.89,
					628.72
				],
				2006: [
					88.8,
					103.35,
					1461.81,
					276.77,
					634.94,
					939.43,
					672.76,
					750.14,
					93.81,
					1545.05,
					925.1,
					1011.03,
					865.98,
					786.14,
					2138.9,
					1916.74,
					1140.41,
					1272.2,
					1532.17,
					1032.47,
					323.48,
					386.38,
					1595.48,
					382.06,
					724.4,
					50.9,
					484.81,
					334,
					67.55,
					79.54,
					527.8
				],
				2005: [
					88.68,
					112.38,
					1400,
					262.42,
					589.56,
					882.41,
					625.61,
					684.6,
					90.26,
					1461.51,
					892.83,
					966.5,
					827.36,
					727.37,
					1963.51,
					1892.01,
					1082.13,
					1100.65,
					1428.27,
					912.5,
					300.75,
					463.4,
					1481.14,
					368.94,
					661.69,
					48.04,
					435.77,
					308.06,
					65.34,
					72.07,
					509.99
				],
				2004: [
					87.36,
					105.28,
					1370.43,
					276.3,
					522.8,
					798.43,
					568.69,
					605.79,
					83.45,
					1367.58,
					814.1,
					950.5,
					786.84,
					664.5,
					1778.45,
					1649.29,
					1020.09,
					1022.45,
					1248.59,
					817.88,
					278.76,
					428.05,
					1379.93,
					334.5,
					607.75,
					44.3,
					387.88,
					286.78,
					60.7,
					65.33,
					461.26
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					494.6,
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					302.66,
					237.91,
					48.47,
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					412.9
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					84.21,
					956.84,
					197.8,
					374.69,
					590.2,
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					281.1,
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					6935.59,
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					5234,
					6367.69,
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					163.92,
					5446.1,
					1984.97,
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					827.91,
					2592.15
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					3987.84,
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					3993.8,
					5114,
					7906.34,
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					3709.78,
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					7158.84,
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					3861.12,
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					557.12,
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					2070.76
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					2892.53,
					7201.88,
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					5544.14,
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					98.48,
					2986.46,
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					419.03,
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					1647.55
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					2457.08,
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					2374.96,
					4566.83,
					1915.29,
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					80.1,
					2452.44,
					1043.19,
					331.91,
					351.58,
					1459.3
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					2026.51,
					2135.07,
					5271.57,
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					1773.21,
					3869.4,
					1580.83,
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					7164.75,
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					63.52,
					1951.36,
					838.56,
					264.61,
					281.05,
					1164.79
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					1853.58,
					1685.93,
					4301.73,
					1919.4,
					1248.27,
					3061.62,
					1329.68,
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					52.74,
					1553.1,
					713.3,
					211.7,
					244.05,
					914.47
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					1487.15,
					1337.31,
					3417.56,
					1463.38,
					967.49,
					2898.89,
					1098.37,
					2084.7,
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					6787.11,
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					6485.05,
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					47.64,
					1221.17,
					572.02,
					171.92,
					194.27,
					719.54
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					1069.08,
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					1134.31,
					754.78,
					2609.85,
					943.49,
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					941.77,
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					1733.38,
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					934.88,
					32.72,
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					274.82,
					3688.93,
					1536.5,
					470.88,
					702.45,
					1766.69
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					3405.16,
					6068.31,
					2886.92,
					3696.65,
					5891.25,
					2756.26,
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					2412.26,
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					355.93,
					475,
					1421.38
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					2250.04,
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					3770,
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					2178.2,
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					294.91,
					366.18,
					1246.89
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					1902.31,
					3895.36,
					1846.18,
					1934.35,
					3798.26,
					1687.07,
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					5508.48,
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					1649.2,
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					989.38,
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					159.76,
					1806.36,
					900.16,
					249.04,
					294.78,
					1058.16
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					1658.19,
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					1542.26,
					3295.45,
					1413.83,
					1857.42,
					4776.2,
					6612.22,
					5360.1,
					2137.77,
					2551.41,
					1411.92,
					5924.74,
					3181.27,
					2655.94,
					2882.88,
					9772.5,
					1560.92,
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					815.32,
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					137.24,
					1546.59,
					787.36,
					213.37,
					259.49,
					929.41
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					1319.76,
					2805.47,
					1375.67,
					1270,
					2811.95,
					1223.64,
					1657.77,
					4097.26,
					5198.03,
					4584.22,
					1963.9,
					2206.02,
					1225.8,
					4764.7,
					2722.4,
					2292.55,
					2428.95,
					8335.3,
					1361.92,
					335.3,
					1229.62,
					2510.3,
					661.8,
					1192.53,
					123.3,
					1234.6,
					688.41,
					193.7,
					227.73,
					833.36
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					3435.95,
					1150.81,
					2439.68,
					1176.65,
					1000.79,
					2487.85,
					1075.48,
					1467.9,
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					1638.42,
					1949.91,
					1043.08,
					4112.43,
					2358.86,
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					7178.94,
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					293.85,
					1081.35,
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					558.28,
					1013.76,
					96.76,
					1063.89,
					589.91,
					169.81,
					195.46,
					753.91
				],
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					2982.57,
					997.47,
					2149.75,
					992.69,
					811.47,
					2258.17,
					958.88,
					1319.4,
					3038.9,
					3891.92,
					3227.99,
					1399.02,
					1765.8,
					972.73,
					3700.52,
					1978.37,
					1795.93,
					1780.79,
					6343.94,
					1074.85,
					270.96,
					956.12,
					1943.68,
					480.37,
					914.5,
					89.56,
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					148.83,
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					492.1,
					1019.68,
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					1677.13,
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					987,
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					222.31,
					17.44,
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					176.22
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					377.59,
					697.79,
					192,
					309.25,
					733.37,
					212.32,
					391.89,
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					2813.95,
					405.79,
					188.33,
					266.38,
					558.56,
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					223.45,
					14.54,
					315.95,
					110.02,
					25.41,
					60.53,
					143.44
				],
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					308.73,
					612.4,
					173.31,
					286.65,
					605.27,
					200.14,
					301.18,
					1237.56,
					2025.39,
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					497.94,
					656.61,
					305.9,
					1329.59,
					622.98,
					546.11,
					400.11,
					2470.63,
					348.98,
					121.76,
					229.09,
					548.14,
					136.15,
					205.14,
					13.28,
					239.92,
					101.37,
					23.05,
					47.56,
					115.23
				],
				2008: [
					844.59,
					227.88,
					513.81,
					166.04,
					273.3,
					500.81,
					182.7,
					244.47,
					939.34,
					1626.13,
					1052.03,
					431.27,
					506.98,
					281.96,
					1104.95,
					512.42,
					526.88,
					340.07,
					2057.45,
					282.96,
					95.6,
					191.21,
					453.63,
					104.81,
					195.48,
					15.08,
					193.27,
					93.8,
					19.96,
					38.85,
					89.79
				],
				2007: [
					821.5,
					183.44,
					467.97,
					134.12,
					191.01,
					410.43,
					153.03,
					225.81,
					958.06,
					1365.71,
					981.42,
					366.57,
					511.5,
					225.96,
					953.69,
					447.44,
					409.65,
					301.8,
					2029.77,
					239.45,
					67.19,
					196.06,
					376.84,
					93.19,
					193.59,
					13.24,
					153.98,
					83.52,
					16.98,
					29.49,
					91.28
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				2006: [
					658.3,
					156.64,
					397.14,
					117.01,
					136.5,
					318.54,
					131.01,
					194.7,
					773.61,
					1017.91,
					794.41,
					281.98,
					435.22,
					184.67,
					786.51,
					348.7,
					294.73,
					254.81,
					1722.07,
					192.2,
					44.45,
					158.2,
					336.2,
					80.24,
					165.92,
					11.92,
					125.2,
					73.21,
					15.17,
					25.53,
					68.9
				],
				2005: [
					493.73,
					122.67,
					330.87,
					106,
					98.75,
					256.77,
					112.29,
					163.34,
					715.97,
					799.73,
					688.86,
					231.66,
					331.8,
					171.88,
					664.9,
					298.19,
					217.17,
					215.63,
					1430.37,
					165.05,
					38.2,
					143.88,
					286.23,
					76.38,
					148.69,
					10.02,
					108.62,
					63.78,
					14.1,
					22.97,
					55.79
				],
				2004: [
					436.11,
					106.14,
					231.08,
					95.1,
					73.81,
					203.1,
					97.93,
					137.74,
					666.3,
					534.17,
					587.83,
					188.28,
					248.44,
					167.2,
					473.27,
					236.44,
					204.8,
					191.5,
					1103.75,
					122.52,
					30.64,
					129.12,
					264.3,
					68.3,
					116.54,
					5.8,
					95.9,
					56.84,
					13,
					20.78,
					53.55
				],
				2003: [
					341.88,
					92.31,
					185.19,
					78.73,
					61.05,
					188.49,
					91.99,
					127.2,
					487.82,
					447.47,
					473.16,
					162.63,
					215.84,
					138.02,
					418.21,
					217.58,
					176.8,
					186.49,
					955.66,
					100.93,
					25.14,
					113.69,
					231.72,
					59.86,
					103.79,
					4.35,
					83.9,
					48.09,
					11.41,
					16.85,
					47.84
				],
				2002: [
					298.02,
					73.04,
					140.89,
					65.83,
					51.48,
					130.94,
					76.11,
					118.7,
					384.86,
					371.09,
					360.63,
					139.18,
					188.09,
					125.27,
					371.13,
					199.31,
					145.17,
					165.29,
					808.16,
					82.83,
					21.45,
					90.48,
					210.82,
					53.49,
					95.68,
					3.42,
					77.68,
					41.52,
					9.74,
					13.46,
					43.04
				]
			});

			dataMap.dataFinancial = dataFormatter({
				//max : 3200,
				2011: [
					2215.41,
					756.5,
					746.01,
					519.32,
					447.46,
					755.57,
					207.65,
					370.78,
					2277.4,
					2600.11,
					2730.29,
					503.85,
					862.41,
					357.44,
					1640.41,
					868.2,
					674.57,
					501.09,
					2916.13,
					445.37,
					105.24,
					704.66,
					868.15,
					297.27,
					456.23,
					31.7,
					432.11,
					145.05,
					62.56,
					134.18,
					288.77
				],
				2010: [
					1863.61,
					572.99,
					615.42,
					448.3,
					346.44,
					639.27,
					190.12,
					304.59,
					1950.96,
					2105.92,
					2326.58,
					396.17,
					767.58,
					241.49,
					1361.45,
					697.68,
					561.27,
					463.16,
					2658.76,
					384.53,
					78.12,
					496.56,
					654.7,
					231.51,
					375.08,
					27.08,
					384.75,
					100.54,
					54.53,
					97.87,
					225.2
				],
				2009: [
					1603.63,
					461.2,
					525.67,
					361.64,
					291.1,
					560.2,
					180.83,
					227.54,
					1804.28,
					1596.98,
					1899.33,
					359.6,
					612.2,
					165.1,
					1044.9,
					499.92,
					479.11,
					402.57,
					2283.29,
					336.82,
					65.73,
					389.97,
					524.63,
					194.44,
					351.74,
					23.17,
					336.21,
					88.27,
					45.63,
					75.54,
					198.87
				],
				2008: [
					1519.19,
					368.1,
					420.74,
					290.91,
					219.09,
					455.07,
					147.24,
					177.43,
					1414.21,
					1298.48,
					1653.45,
					313.81,
					497.65,
					130.57,
					880.28,
					413.83,
					393.05,
					334.32,
					1972.4,
					249.01,
					47.33,
					303.01,
					411.14,
					151.55,
					277.66,
					22.42,
					287.16,
					72.49,
					36.54,
					64.8,
					171.97
				],
				2007: [
					1302.77,
					288.17,
					347.65,
					218.73,
					148.3,
					386.34,
					126.03,
					155.48,
					1209.08,
					1054.25,
					1251.43,
					223.85,
					385.84,
					101.34,
					734.9,
					302.31,
					337.27,
					260.14,
					1705.08,
					190.73,
					34.43,
					247.46,
					359.11,
					122.25,
					168.55,
					11.51,
					231.03,
					61.6,
					27.67,
					51.05,
					149.22
				],
				2006: [
					982.37,
					186.87,
					284.04,
					169.63,
					108.21,
					303.41,
					100.75,
					74.17,
					825.2,
					653.25,
					906.37,
					166.01,
					243.9,
					79.75,
					524.94,
					219.72,
					174.99,
					204.72,
					899.91,
					129.14,
					16.37,
					213.7,
					299.5,
					89.43,
					143.62,
					6.44,
					152.25,
					50.51,
					23.69,
					36.99,
					99.25
				],
				2005: [
					840.2,
					147.4,
					213.47,
					135.07,
					72.52,
					232.85,
					83.63,
					35.03,
					675.12,
					492.4,
					686.32,
					127.05,
					186.12,
					69.55,
					448.36,
					181.74,
					127.32,
					162.37,
					661.81,
					91.93,
					13.16,
					185.18,
					262.26,
					73.67,
					130.5,
					7.57,
					127.58,
					44.73,
					20.36,
					32.25,
					80.34
				],
				2004: [
					713.79,
					136.97,
					209.1,
					110.29,
					55.89,
					188.04,
					77.17,
					32.2,
					612.45,
					440.5,
					523.49,
					94.1,
					171,
					65.1,
					343.37,
					170.82,
					118.85,
					118.64,
					602.68,
					74,
					11.56,
					162.38,
					236.5,
					60.3,
					118.4,
					5.4,
					90.1,
					42.99,
					19,
					27.92,
					70.3
				],
				2003: [
					635.56,
					112.79,
					199.87,
					118.48,
					55.89,
					145.38,
					73.15,
					32.2,
					517.97,
					392.11,
					451.54,
					87.45,
					150.09,
					64.31,
					329.71,
					165.11,
					107.31,
					99.35,
					534.28,
					61.59,
					10.68,
					147.04,
					206.24,
					48.01,
					105.48,
					4.74,
					77.87,
					42.31,
					17.98,
					24.8,
					64.92
				],
				2002: [
					561.91,
					76.86,
					179.6,
					124.1,
					48.39,
					137.18,
					75.45,
					31.6,
					485.25,
					368.86,
					347.53,
					81.85,
					138.28,
					76.51,
					310.07,
					158.77,
					96.95,
					92.43,
					454.65,
					35.86,
					10.08,
					134.52,
					183.13,
					41.45,
					102.39,
					2.81,
					67.3,
					42.08,
					16.75,
					21.45,
					52.18
				]
			});
			myChart.setOption({
				baseOption: {
					timeline: {
						axisType: 'category',
						// realtime: false,
						// loop: false,
						autoPlay: true,
						// currentIndex: 2,
						playInterval: 1000,
						// controlStyle: {
						//     position: 'left'
						// },
						data: [
							'2002-01-01',
							'2003-01-01',
							'2004-01-01',
							{
								value: '2005-01-01',
								tooltip: {
									formatter: '{b} GDP达到一个高度'
								},
								symbol: 'diamond',
								symbolSize: 16
							},
							'2006-01-01',
							'2007-01-01',
							'2008-01-01',
							'2009-01-01',
							'2010-01-01',
							{
								value: '2011-01-01',
								tooltip: {
									formatter: function(params) {
										return params.name + 'GDP达到又一个高度';
									}
								},
								symbol: 'diamond',
								symbolSize: 18
							}
						],
						label: {
							formatter: function(s) {
								return new Date(s).getFullYear();
							}
						}
					},
					title: {
						subtext: '数据来自国家统计局'
					},
					tooltip: {},
					legend: {
						left: 'right',
						data: ['第一产业', '第二产业', '第三产业', 'GDP', '金融', '房地产'],
						selected: {
							GDP: false,
							金融: false,
							房地产: false
						}
					},
					calculable: true,
					grid: {
						top: 80,
						bottom: 100,
						tooltip: {
							trigger: 'axis',
							axisPointer: {
								type: 'shadow',
								label: {
									show: true,
									formatter: function(params) {
										return params.value.replace('\n', '');
									}
								}
							}
						}
					},
					xAxis: [
						{
							type: 'category',
							axisLabel: {
								interval: 0
							},
							data: [
								'北京',
								'\n天津',
								'河北',
								'\n山西',
								'内蒙古',
								'\n辽宁',
								'吉林',
								'\n黑龙江',
								'上海',
								'\n江苏',
								'浙江',
								'\n安徽',
								'福建',
								'\n江西',
								'山东',
								'\n河南',
								'湖北',
								'\n湖南',
								'广东',
								'\n广西',
								'海南',
								'\n重庆',
								'四川',
								'\n贵州',
								'云南',
								'\n西藏',
								'陕西',
								'\n甘肃',
								'青海',
								'\n宁夏',
								'新疆'
							],
							splitLine: {
								show: false
							}
						}
					],
					yAxis: [
						{
							type: 'value',
							name: 'GDP（亿元）'
						}
					],
					series: [
						{
							name: 'GDP',
							type: 'bar'
						},
						{
							name: '金融',
							type: 'bar'
						},
						{
							name: '房地产',
							type: 'bar'
						},
						{
							name: '第一产业',
							type: 'bar'
						},
						{
							name: '第二产业',
							type: 'bar'
						},
						{
							name: '第三产业',
							type: 'bar'
						},
						{
							name: 'GDP占比',
							type: 'pie',
							center: ['75%', '35%'],
							radius: '28%',
							z: 100
						}
					]
				},
				options: [
					{
						title: {
							text: '2002全国宏观经济指标'
						},
						series: [
							{
								data: dataMap.dataGDP['2002']
							},
							{
								data: dataMap.dataFinancial['2002']
							},
							{
								data: dataMap.dataEstate['2002']
							},
							{
								data: dataMap.dataPI['2002']
							},
							{
								data: dataMap.dataSI['2002']
							},
							{
								data: dataMap.dataTI['2002']
							},
							{
								data: [
									{
										name: '第一产业',
										value: dataMap.dataPI['2002sum']
									},
									{
										name: '第二产业',
										value: dataMap.dataSI['2002sum']
									},
									{
										name: '第三产业',
										value: dataMap.dataTI['2002sum']
									}
								]
							}
						]
					},
					{
						title: {
							text: '2003全国宏观经济指标'
						},
						series: [
							{
								data: dataMap.dataGDP['2003']
							},
							{
								data: dataMap.dataFinancial['2003']
							},
							{
								data: dataMap.dataEstate['2003']
							},
							{
								data: dataMap.dataPI['2003']
							},
							{
								data: dataMap.dataSI['2003']
							},
							{
								data: dataMap.dataTI['2003']
							},
							{
								data: [
									{
										name: '第一产业',
										value: dataMap.dataPI['2003sum']
									},
									{
										name: '第二产业',
										value: dataMap.dataSI['2003sum']
									},
									{
										name: '第三产业',
										value: dataMap.dataTI['2003sum']
									}
								]
							}
						]
					},
					{
						title: {
							text: '2004全国宏观经济指标'
						},
						series: [
							{
								data: dataMap.dataGDP['2004']
							},
							{
								data: dataMap.dataFinancial['2004']
							},
							{
								data: dataMap.dataEstate['2004']
							},
							{
								data: dataMap.dataPI['2004']
							},
							{
								data: dataMap.dataSI['2004']
							},
							{
								data: dataMap.dataTI['2004']
							},
							{
								data: [
									{
										name: '第一产业',
										value: dataMap.dataPI['2004sum']
									},
									{
										name: '第二产业',
										value: dataMap.dataSI['2004sum']
									},
									{
										name: '第三产业',
										value: dataMap.dataTI['2004sum']
									}
								]
							}
						]
					},
					{
						title: {
							text: '2005全国宏观经济指标'
						},
						series: [
							{
								data: dataMap.dataGDP['2005']
							},
							{
								data: dataMap.dataFinancial['2005']
							},
							{
								data: dataMap.dataEstate['2005']
							},
							{
								data: dataMap.dataPI['2005']
							},
							{
								data: dataMap.dataSI['2005']
							},
							{
								data: dataMap.dataTI['2005']
							},
							{
								data: [
									{
										name: '第一产业',
										value: dataMap.dataPI['2005sum']
									},
									{
										name: '第二产业',
										value: dataMap.dataSI['2005sum']
									},
									{
										name: '第三产业',
										value: dataMap.dataTI['2005sum']
									}
								]
							}
						]
					},
					{
						title: {
							text: '2006全国宏观经济指标'
						},
						series: [
							{
								data: dataMap.dataGDP['2006']
							},
							{
								data: dataMap.dataFinancial['2006']
							},
							{
								data: dataMap.dataEstate['2006']
							},
							{
								data: dataMap.dataPI['2006']
							},
							{
								data: dataMap.dataSI['2006']
							},
							{
								data: dataMap.dataTI['2006']
							},
							{
								data: [
									{
										name: '第一产业',
										value: dataMap.dataPI['2006sum']
									},
									{
										name: '第二产业',
										value: dataMap.dataSI['2006sum']
									},
									{
										name: '第三产业',
										value: dataMap.dataTI['2006sum']
									}
								]
							}
						]
					},
					{
						title: {
							text: '2007全国宏观经济指标'
						},
						series: [
							{
								data: dataMap.dataGDP['2007']
							},
							{
								data: dataMap.dataFinancial['2007']
							},
							{
								data: dataMap.dataEstate['2007']
							},
							{
								data: dataMap.dataPI['2007']
							},
							{
								data: dataMap.dataSI['2007']
							},
							{
								data: dataMap.dataTI['2007']
							},
							{
								data: [
									{
										name: '第一产业',
										value: dataMap.dataPI['2007sum']
									},
									{
										name: '第二产业',
										value: dataMap.dataSI['2007sum']
									},
									{
										name: '第三产业',
										value: dataMap.dataTI['2007sum']
									}
								]
							}
						]
					},
					{
						title: {
							text: '2008全国宏观经济指标'
						},
						series: [
							{
								data: dataMap.dataGDP['2008']
							},
							{
								data: dataMap.dataFinancial['2008']
							},
							{
								data: dataMap.dataEstate['2008']
							},
							{
								data: dataMap.dataPI['2008']
							},
							{
								data: dataMap.dataSI['2008']
							},
							{
								data: dataMap.dataTI['2008']
							},
							{
								data: [
									{
										name: '第一产业',
										value: dataMap.dataPI['2008sum']
									},
									{
										name: '第二产业',
										value: dataMap.dataSI['2008sum']
									},
									{
										name: '第三产业',
										value: dataMap.dataTI['2008sum']
									}
								]
							}
						]
					},
					{
						title: {
							text: '2009全国宏观经济指标'
						},
						series: [
							{
								data: dataMap.dataGDP['2009']
							},
							{
								data: dataMap.dataFinancial['2009']
							},
							{
								data: dataMap.dataEstate['2009']
							},
							{
								data: dataMap.dataPI['2009']
							},
							{
								data: dataMap.dataSI['2009']
							},
							{
								data: dataMap.dataTI['2009']
							},
							{
								data: [
									{
										name: '第一产业',
										value: dataMap.dataPI['2009sum']
									},
									{
										name: '第二产业',
										value: dataMap.dataSI['2009sum']
									},
									{
										name: '第三产业',
										value: dataMap.dataTI['2009sum']
									}
								]
							}
						]
					},
					{
						title: {
							text: '2010全国宏观经济指标'
						},
						series: [
							{
								data: dataMap.dataGDP['2010']
							},
							{
								data: dataMap.dataFinancial['2010']
							},
							{
								data: dataMap.dataEstate['2010']
							},
							{
								data: dataMap.dataPI['2010']
							},
							{
								data: dataMap.dataSI['2010']
							},
							{
								data: dataMap.dataTI['2010']
							},
							{
								data: [
									{
										name: '第一产业',
										value: dataMap.dataPI['2010sum']
									},
									{
										name: '第二产业',
										value: dataMap.dataSI['2010sum']
									},
									{
										name: '第三产业',
										value: dataMap.dataTI['2010sum']
									}
								]
							}
						]
					},
					{
						title: {
							text: '2011全国宏观经济指标'
						},
						series: [
							{
								data: dataMap.dataGDP['2011']
							},
							{
								data: dataMap.dataFinancial['2011']
							},
							{
								data: dataMap.dataEstate['2011']
							},
							{
								data: dataMap.dataPI['2011']
							},
							{
								data: dataMap.dataSI['2011']
							},
							{
								data: dataMap.dataTI['2011']
							},
							{
								data: [
									{
										name: '第一产业',
										value: dataMap.dataPI['2011sum']
									},
									{
										name: '第二产业',
										value: dataMap.dataSI['2011sum']
									},
									{
										name: '第三产业',
										value: dataMap.dataTI['2011sum']
									}
								]
							}
						]
					}
				]
			});
		},

		init4() {
			document.getElementById('myChart4').setAttribute('_echarts_instance_', '');
			var myChart = echarts.init(document.getElementById('myChart4'));
			var builderJson = {
				all: 10887,
				charts: {
					map: 3237,
					lines: 2164,
					bar: 7561,
					line: 7778,
					pie: 7355,
					scatter: 2405,
					candlestick: 1842,
					radar: 2090,
					heatmap: 1762,
					treemap: 1593,
					graph: 2060,
					boxplot: 1537,
					parallel: 1908,
					gauge: 2107,
					funnel: 1692,
					sankey: 1568
				},
				components: {
					geo: 2788,
					title: 9575,
					legend: 9400,
					tooltip: 9466,
					grid: 9266,
					markPoint: 3419,
					markLine: 2984,
					timeline: 2739,
					dataZoom: 2744,
					visualMap: 2466,
					toolbox: 3034,
					polar: 1945
				},
				ie: 9743
			};

			var downloadJson = {
				'echarts.min.js': 17365,
				'echarts.simple.min.js': 4079,
				'echarts.common.min.js': 6929,
				'echarts.js': 14890
			};

			var themeJson = {
				'dark.js': 1594,
				'infographic.js': 925,
				'shine.js': 1608,
				'roma.js': 721,
				'macarons.js': 2179,
				'vintage.js': 1982
			};

			var waterMarkText = 'ECHARTS';

			var canvas = document.createElement('canvas');
			var ctx = canvas.getContext('2d');
			canvas.width = canvas.height = 100;
			ctx.textAlign = 'center';
			ctx.textBaseline = 'middle';
			ctx.globalAlpha = 0.08;
			ctx.font = '20px Microsoft Yahei';
			ctx.translate(50, 50);
			ctx.rotate(-Math.PI / 4);
			ctx.fillText(waterMarkText, 0, 0);
			myChart.setOption({
				backgroundColor: {
					type: 'pattern',
					image: canvas,
					repeat: 'repeat'
				},
				tooltip: {},
				title: [
					{
						text: '在线构建',
						subtext: '总计 ' + builderJson.all,
						left: '25%',
						textAlign: 'center'
					},
					{
						text: '各版本下载',
						subtext:
							'总计 ' +
							Object.keys(downloadJson).reduce(function(all, key) {
								return all + downloadJson[key];
							}, 0),
						left: '75%',
						textAlign: 'center'
					},
					{
						text: '主题下载',
						subtext:
							'总计 ' +
							Object.keys(themeJson).reduce(function(all, key) {
								return all + themeJson[key];
							}, 0),
						left: '75%',
						top: '50%',
						textAlign: 'center'
					}
				],
				grid: [
					{
						top: 50,
						width: '50%',
						bottom: '45%',
						left: 10,
						containLabel: true
					},
					{
						top: '55%',
						width: '50%',
						bottom: 0,
						left: 10,
						containLabel: true
					}
				],
				xAxis: [
					{
						type: 'value',
						max: builderJson.all,
						splitLine: {
							show: false
						}
					},
					{
						type: 'value',
						max: builderJson.all,
						gridIndex: 1,
						splitLine: {
							show: false
						}
					}
				],
				yAxis: [
					{
						type: 'category',
						data: Object.keys(builderJson.charts),
						axisLabel: {
							interval: 0,
							rotate: 30
						},
						splitLine: {
							show: false
						}
					},
					{
						gridIndex: 1,
						type: 'category',
						data: Object.keys(builderJson.components),
						axisLabel: {
							interval: 0,
							rotate: 30
						},
						splitLine: {
							show: false
						}
					}
				],
				series: [
					{
						type: 'bar',
						stack: 'chart',
						z: 3,
						label: {
							normal: {
								position: 'right',
								show: true
							}
						},
						data: Object.keys(builderJson.charts).map(function(key) {
							return builderJson.charts[key];
						})
					},
					{
						type: 'bar',
						stack: 'chart',
						silent: true,
						itemStyle: {
							normal: {
								color: '#eee'
							}
						},
						data: Object.keys(builderJson.charts).map(function(key) {
							return builderJson.all - builderJson.charts[key];
						})
					},
					{
						type: 'bar',
						stack: 'component',
						xAxisIndex: 1,
						yAxisIndex: 1,
						z: 3,
						label: {
							normal: {
								position: 'right',
								show: true
							}
						},
						data: Object.keys(builderJson.components).map(function(key) {
							return builderJson.components[key];
						})
					},
					{
						type: 'bar',
						stack: 'component',
						silent: true,
						xAxisIndex: 1,
						yAxisIndex: 1,
						itemStyle: {
							normal: {
								color: '#eee'
							}
						},
						data: Object.keys(builderJson.components).map(function(key) {
							return builderJson.all - builderJson.components[key];
						})
					},
					{
						type: 'pie',
						radius: [0, '30%'],
						center: ['75%', '25%'],
						data: Object.keys(downloadJson).map(function(key) {
							return {
								name: key.replace('.js', ''),
								value: downloadJson[key]
							};
						})
					},
					{
						type: 'pie',
						radius: [0, '30%'],
						center: ['75%', '75%'],
						data: Object.keys(themeJson).map(function(key) {
							return {
								name: key.replace('.js', ''),
								value: themeJson[key]
							};
						})
					}
				]
			});
		}
	}
};
</script>

<style lang="scss" scoped>
@import '@admin/assets/css/common';
.page {
	ul {
		display: flex;
		justify-content: space-around;
		flex-wrap: wrap;
		li {
			width: 20%;
			height: 100px;
			background: #fff;
			border-radius: 10px;
			box-shadow: 1px 1px 10px #ccc;
			color: red;
		}
	}
}
</style>
